(212) 998-0452
TBA
KMC 8-54
Class web site: http://www.stern.nyu.edu/~jsimonof/classes/2301
W, 6:00pm to 9:00pm
Tisch T-LC25
Schedule exceptions
Note that this class is scheduled based on the Stern graduate part-time MBA calendar. As such, the first class session will be on February 13, 2013, and the last class session will be on May 8, 2013.
Class will not be held on March 20, 2013.
This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. The goal of the class is that students begin to develop the skills to be able to collect, organize, analyze, and interpret regression data.
The pre-requisite for this course is an introductory statistics class that includes discussion of descriptive statistics and univariate statistical inference (confidence intervals, prediction intervals, and hypothesis testing), and exposure to simple regression methods.
It is crucially important that all students review basic regression material before the first class. I will assume a basic understanding of the simple regression model from the beginning of the class. You should review this material from your introductory statistics course before the first class session. You should download, print out, and read the following handouts from the class website: Regression - the basics and Purchasing power parity - is it true?. You are responsible for all of the material in those handouts, although we will briefly discuss them in class. You should also download Homework 1 and answer all of the questions. I will give out the answers to these questions on the first day of class.
Chapters refer to the Chatterjee and Simonoff book. Corresponding class sessions given are only approximate.
Classes 1-3
1. Simple and multiple regression - Chapter 1
Classes 3-4
2. Checking assumptions of regression - Chapters 1, 2, 3
Classes 4-8
3. Addressing violation of assumptions: choosing the correct predictors (model selection), autocorrelation - Chapters 2, 4, 5
Classes 8-9
4. Analysis of variance and covariance and nonconstant variance - Chapters 6,7
Classes 10-12
5. Modeling group membership: logistic regression - Chapters 8, 9
Texts
Samprit Chatterjee and Jeffrey S. Simonoff, Handbook of Regression Analysis, John Wiley and Sons (2013). [Highly recommended, but not required; you can do all of the work required for the class without it. In any event, I believe that it is a useful applied guide to have.]
Samprit Chatterjee, Mark S. Handcock and Jeffrey S. Simonoff, A Casebook for a First Course in Statistics and Data Analysis, John Wiley and Sons (1995). [Optional; see discussion below.]
Some of you might not have very much experience in reading or writing statistical reports, a skill that you will need for this class. The CHS Casebook gives many examples of such reports. I urge you to read some of the cases that appear in the book to see what such reports look like if you have concerns about this. Examples include the first few cases in each of the sections Data analysis, Applied probability, Statistical inference, and Regression analysis. You will find that my reports are somewhat "chatty" - it's perfectly appropriate (even desirable) for you to write such reports for this class, but you should be aware that the reports you might write for other classes might need to be more factual and to-the-point. An excellent way to get "up to speed" in your statistical computing is to work through these cases on the computer. If you are comfortable with your ability to write such reports, you will probably find the Casebook to be of little use to you.
Software
The course will be very heavily computer oriented; if you have not used a statistical package before, you may be in for some rough going. The "official" package for the course is Minitab, which is available online, for rent, and for purchase at the bookstore (I highly recommend that you either rent or purchase the package). You may use any package you wish, on any machine that you wish, as long as it performs the necessary calculations; any deficiencies on the part of the package are the responsibility of the student. Note that the "student version" of Minitab cannot do all of the analyses required for the class. I can provide additional support for Minitab, S-Plus, and R, but relatively little for SAS, and none at all for SPSS, Stata, Systat, and STATISTICA (although these packages are able to perform all of the necessary modeling methods for this class). Excel will not be an acceptable tool for analyses in this class, and the student version of Minitab is missing some necessary techniques that are included in the full version.
The course grade will be based on homeworks/projects only. For most assignments, you will be responsible for obtaining your own data. Generally speaking, you will have roughly two weeks to complete each assignment from when it is given out, although in some cases it will be expected that material that is covered in class after the assignment is handed out (but before it is due) will be used by you in the assignment. Assignments must be typed or word processed; handwritten assignments will not be accepted.
THIS COURSE IS LIKELY TO BE TIME-CONSUMING! If you're taking a particularly heavy course load this semester, or are going to be doing a lot of traveling (work-related, for example), this is probably not the course for you! In particular, since it takes time to build up the knowledge necessary for adequate multiple regression analysis, the homeworks will be relatively widely separated in the first half of the semester, but will come more rapidly in the second half.
Grades will be determined based on a class-wide curve (that is, there will not be separate curves for undergraduates and graduate students). A friendly piece of advice: don't hand in the assignments late! That is the quickest way to get in trouble in a course like this. An assignment is considered late if it is turned in after I have left Stern for the day on the day that it is due. There will be progressively bigger penalties for increasing amount of lateness of an assignment (2 points out of 10 up to one week late, 4 points out of 10 up to two weeks late). No assignments will be accepted for credit more than two weeks late under any circumstances. Work responsibilities in general, including work-related travel in particular, will not be accepted as an excuse for lateness of an assignment; it is your responsibility to get the assignment to me on time, even if you are not at Stern that day.
The final grade for the course will be based on the grades on the assigned homeworks only; there will be no opportunities for makeup or extra credit work, and an incomplete grade for the course will not be considered simply to make up assignments that were not done. Thus, assignments for which you receive no credit will have a strong detrimental effect on your grade, and as few as two such assignments could result in a failing grade in the course. The actual curve used in the course will depend on the performance of the class, but in the past the lower cutoff for A grades (A and A-) has been roughly 8.5 (out of 10), while the lower cutoff for B grades (B+, B, and B-) has been roughly 7.5 (there is no guarantee that these cutoffs will apply this semester, however).
Attendance
Classroom Norms
Integrity is critical to the learning process and to all that we do here at NYU Stern. As members of our community, all students agree to abide by the NYU Stern Student Code of Conduct, which includes a commitment to:
The entire Stern Student Code of Conduct applies to all students enrolled in Stern courses and can be found here:
Undergraduate College: http://www.stern.nyu.edu/uc/codeofconduct
Graduate Programs: http://w4.stern.nyu.edu/studentactivities/involved.cfm?doc_id=102505
To help ensure the integrity of our learning community, prose assignments you submit to Blackboard will be submitted to Turnitin. Turnitin will compare your submission to a database of prior submissions to Turnitin, current and archived Web pages, periodicals, journals, and publications. Additionally, your document will become part of the Turnitin database.
Your class may be recorded for educational purposes
If you have a qualified disability and will require academic accommodation of any kind during this course, you must notify me at the beginning of the course and provide a letter from the Moses Center for Students with Disabilities (CSD, 998-4980, www.nyu.edu/csd) verifying your registration and outlining the accommodations they recommend. If you will need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be guaranteed accommodation.
General Behavior
The School expects that students will conduct themselves with respect and professionalism toward faculty, students, and others present in class and will follow the rules laid down by the instructor for classroom behavior. Students who fail to do so may be asked to leave the classroom.
Collaboration on Graded Assignments
Students may not work together on graded assignments. It is not permitted for you to show your assignment to anyone else, whether they are in the class or not, or for you to look at anyone else's assignment, whether that assignment is from this semester's class or a previous class.
Course Evaluations
Course evaluations are important to us and to students who come after you. Please complete them thoughtfully.
If you have any questions about the grade you have received on a homework, you must raise it with me by the end of the class session following the session in which the homework was returned to the class; no grading adjustments will be considered after that time.